Intel Foundry Advanced Device Development Engineer

Intel Intel · Semiconductors · Taipei, Taiwan +1

The role involves designing, executing, and analyzing experiments in a semiconductor cleanroom to meet engineering specifications for advanced device development. Responsibilities include setting performance targets, assessing silicon readiness, defining test structures, and using data analysis techniques (statistics, data mining) on electrical-test, yield, and fab data to guide future development. Experience with semiconductor materials, fabrication, device physics, and electrical characterization is required, with preferred experience in advanced transistor structures, simulation, SPC/DOE, and analytics/scripting languages.

What you'd actually do

  1. Set advanced device performance targets for Process Design Kit releases and Interface with Design and Foundry partners to address circuit-level issues.
  2. Assess Silicon-to-Simulation health and readiness for High Volume Manufacturing.
  3. Define robust test structures and test programs to validate advanced design rules and reliably extract key device parameters.
  4. Use Statistics, Data Mining, and other data analysis techniques to collect, explore, and extract insights from Electrical-test, yield, and fab data to define future development direction.

Skills

Required

  • master's degree with 2-years' industry experiences or Ph.D. degree in electrical engineering (EE), electrical and computer engineering (ECE), electrical engineering and computer science (EECS) directly related to Semiconductor field.
  • Semiconductor materials, fabrication, and device physics
  • Electrical characterization of Semiconductor Devices (transistor, diode, etc.)

Nice to have

  • Advanced Transistor Device Structures and Device Physics.
  • Process monitoring Test structures design and layout experience.
  • Device and circuit simulation.
  • Statistical Process Control (SPC) or Design of Experiments (DOE) principles and engineering analysis tools.
  • database structures
  • research methods
  • machine learning
  • analytics packages (i.e., JMP, MATLAB, Octave)
  • scripting languages (i.e., Python, JSL, Perl, TCL)
  • programming languages (i.e., SQL, C/C++)